Simulation of turbulent convective heat transfer of γ-al2o3/water nanofluid in a tube by ann and anfis models

Roghaye Nazari̇,Hossein Bei̇ki̇,Morteza Esfandyari̇

doi:10.18186/thermal.1067050

Abstract

In order to modeling and predicting heat transfer coefficient in na nofluids, art ificial neural network (ANN) and Adaptive Neuro-fuzzy Inference system (ANFIS) were used in this study. In ANN and ANFIS, Input data are Reynolds number and nanoparticles volume fractions, and output data is heat transfer coefficient. Both of them could predict very well, and there is good agreement between experimental data and predicted data. In ANFIS coefficient of determination (R2), average relative error and mean square error for train data are 0.99, 8.9×10–5 and 6.5476×10–5, respectively, and for test data are one, zero and zero. According to results, by increasing the Reynolds number and volume fractions, the heat transfer coefficient increases. For base fluid in Re = 16300, heat transfer coefficient is 10961.38 W/m2K, and for volume fraction 0.135, heat transfer coefficient is 13947.72 W/m2K, therefore, heat transfer coefficient of nanofluids increased 1.27 time compared to that of base fluid. Results obtained from ANFIS are reliable, and can be used in prediction. Also, for ANN, ARE, MSE and R2 value are, –0.003, 6.38264×10–5 and 0.99, respectively. So, there is good agreement between experimental data and ANN results too. According to errors, can conclude ANFIS is slightly better than ANN.

Highlights

Today, since rising energy cost and environmental pollution, reduction and optimization of energy consumption in the various industrial process become important; using renewable resource energy[1] and alternative green fuel is necessary[2]
Determination coefficient of train data in art ificial neural network (ANN) are slightly lower than that in Adaptive Neuro-fuzzy Inference system (ANFIS), but for test data it become revers. For both ANN and ANFIS can be considered equal to the value of the correlation coefficient
Error value for ANFIS simulation is lower than ANN simulation, so the result in ANFIS is slightly better than ANN

Summary

INTRODUCTION

Since rising energy cost and environmental pollution, reduction and optimization of energy consumption in the various industrial process become important; using renewable resource energy[1] and alternative green fuel is necessary[2]. Nanofluids are homogenous suspensions containing colloidal particles in the nanoscale These fluids noticeable because of their promoted thermo-physical properties such as thermal conductivity compared to prevalent liquids[3]; and modified heat and mass transfer process [4]. Fotukian and Nasr Esfahany[4] measured heat transfer coefficient of γ-AL2O3/ H2O nanofluid in the circular cooper tube with the inner diameter of 5 mm and 0.5 mm thickness Their results showed that addition of small amount of nanoparticles to pure water could be improved the heat transfer performance significantly. Hojjat et al.[5] investigated γ-Al2O3, CuO and TiO2/carboxymethyl cellulose non-Newtonian nanofluids inside the stainless steel tube They observed that heat transfer coefficient increased with Reynolds and Peclet number. Alrashed et al.[7] had modeled the heat transfer and flow of CNT/water nanofluids in backwared-facing contracting channel According to their results, surface temperature was reduced by enhancement of weight percentage of nanotubes and Reynolds numbers. Please do not use the Headers or the Footers because they are reserved for the technical editing by editors

AND DISCUSSION

CONCLUSIONS

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering

Findings

Neuro-fuzzy modeling of the convection heat